Chemometrics in Spectroscopy

Revised Second Edition

2nd Edition - September 30, 2021
Imprint: Academic Press
Authors: Howard Mark, Jerry Workman Jr.
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 1 6 4 - 1
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 1 7 0 - 2

Chemometrics in Spectroscopy, Revised Second Edition provides the reader with the methodology crucial to apply chemometrics to real world data. The book allows scientists using spe… Read more

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Chemometrics in Spectroscopy, Revised Second Edition provides the reader with the methodology crucial to apply chemometrics to real world data. The book allows scientists using spectroscopic instruments to find explanations and solutions to their problems when they are confronted with unexpected and unexplained results. Unlike other books on these topics, it explains the root causes of the phenomena that lead to these results. While books on NIR spectroscopy sometimes cover basic chemometrics, they do not mention many of the advanced topics this book discusses.

This revised second edition has been expanded with 50% more content on advances in the field that have occurred in the last 10 years, including calibration transfer, units of measure in spectroscopy, principal components, clinical data reporting, classical least squares, regression models, spectral transfer, and more.

Cover image
Title page
Table of Contents
Copyright
Dedication
Preface to the First Edition
Preface to the Second Edition
The Book Companion
Chapter 1: A New Beginning …
Abstract
Multivariate Normal Distribution
Section 1: Elementary Matrix Algebra
Chapter 2: Elementary Matrix Algebra: Part 1—Primitive operations: Addition, Subtraction, Multiplication, Division, Inverse, Transpose
Abstract
Matrix Operations
Elementary Operations for Linear Equations
Summary
Chapter 3: Elementary Matrix Algebra: Part 2—Elementary Operations, Inverse of a Matrix
Abstract
Elementary Matrix Operations
Summary
Section 2: Matrix Algebra And Multiple Linear Regression
Chapter 4: Matrix Algebra and Multiple Linear Regression: Part 1—Quasi-Algebraic Operations, Multiple Linear Regression, The Least Squares Method
Abstract
Quasialgebraic Operations
Multiple Linear Regression
The Least Squares Method
Chapter 5: Matrix Algebra and Multiple Linear Regression: Part 2—When There Are More Equations Than Unknowns, The Power of Matrix Mathematics
Abstract
The Power of Matrix Mathematics
Chapter 6: Matrix Algebra and Multiple Linear Regression: Part 3—The Concept of Determinants
Abstract
Chapter 7: Matrix Algebra and Multiple Linear Regression: Part 4—Concluding Remarks, and A Word of Caution
Abstract
A Word of Caution
Section 3: Experimental Designs
Chapter 8: Experimental Designs, Part 1: Introduction
Abstract
Chapter 9: Experimental Designs, Part 2: One-Way ANOVA
Abstract
Chapter 10: Experimental Designs, Part 3: Two-Factor Designs
Abstract
Chapter 11: Experimental Designs, Part 4: Varying Parameters to Expand the Design
Abstract
Introduction to Factorial Designs
Chapter 12: Experimental Designs, Part 5: One-at-a-Time Designs
Abstract
Chapter 13: Experimental Designs, Part 6: Sequential Designs
Abstract
Chapter 14: Experimental Designs, Part 7: β, the Power of a Test
Abstract
Chapter 15: Experimental Designs, Part 8: β, the Power of a Test (Continued)
Abstract
Chapter 16: Experimental Designs, Part 9: Sequential Designs (Concluded)
Abstract
Section 4: Analytic Geometry
Chapter 17: Analytic Geometry: Part 1—The Basics in Two and Three Dimensions
Abstract
The Distance Formula
Direction Notation
The Cosine Function
Direction in 3D Space
Defining Slope in Two Dimensions
Chapter 18: Analytic Geometry: Part 2—Geometric Representation of Vectors and Algebraic Operations
Abstract
Vector Multiplication (Scalar × Vector)
Vector Division (Vector ÷ Scalar)
Vector Addition (Vector + Vector)
Vector Subtraction (Vector − Vector)
Chapter 19: Analytic Geometry: Part 3—Reducing Dimensionality
Abstract
Reducing Dimensionality
3D to 2D by Projection
2D Into 1D by Rotation
Chapter 20: Analytic Geometry: Part 4—The Geometry of Vectors and Matrices
Abstract
Row Vectors in Column Space
Column Vectors in Row Space
Principal Components for Regression Vectors
Section 5: Regression Techniques
Chapter 21: Calculating the Solution for Regression Techniques: Part 1—Multivariate Regression Made Simple
Abstract
Chapter 22: Calculating the Solution for Regression Techniques: Part 2—Principal Component(s) Regression Made Simple
Abstract
Chapter 23: Calculating the Solution for Regression Techniques: Part 3—Partial Least Squares Regression Made Simple
Abstract
Chapter 24: Calculating the Solution for Regression Techniques: Part 4—Singular Value Decomposition
Abstract
Chapter 25: Interlude: Looking Behind and Ahead
Abstract
Chapter 26: A Simple Question
Abstract
Chapter 27: Challenges: Unsolved Problems in Chemometrics
Abstract
So What Are These Problems?
Section 6: Linearity In Calibration
Chapter 28: Linearity in Calibration— Act I—A Thought Experiment Carried Out by Computer Simulation
Abstract
Chapter 29: Linearity in Calibration—Act II Scene I—A Firestorm Erupts and A Theoretical Explanation of Linearity
Abstract
Chapter 30: Linearity in Calibration—Act II Scene II—Details of Reader Responses
Abstract
Chapter 31: Linearity in Calibration—Act II Scene III—Summary of Reader Responses, and Our Commentary on Those Responses☆
Abstract
Chapter 32: Linearity in Calibration—Act II Scene IV—A Summary of Findings and Recommendations for Future Explorations
Abstract
Chapter 33: Linearity in Calibration—Act II Scene V—Effect of (Non) Linearity on PLS Algorithm
Abstract
Section 7: Collaborative Laboratory Studies
Chapter 34: Collaborative Laboratory Studies: Part 1—A Blueprint
Abstract
Experimental Design
Analytical Methods
Results and Data Analysis
Chapter 35: Collaborative Laboratory Studies: Part 2—Using ANOVA
Abstract
ANOVA Test Comparisons for Laboratories and Methods (ANOVA_s4 Worksheet)
ANOVA Test Comparisons (Using ANOVA_s2 Worksheet)
Chapter 36: Collaborative Laboratory Studies: Part 3—Testing for Systematic Error
Abstract
Testing for Systematic Error in a Method: Comparison Test for a Set of Measurements Versus True Value—Spiked Recovery Method (Compare T Worksheet)
Chapter 37: Collaborative Laboratory Studies: Part 4—Ranking Test
Abstract
Ranking Test for Laboratories and Methods (Manual Computations)
Chapter 38: Collaborative Laboratory Studies: Part 5—Efficient Comparison of Two Methods
Abstract
Acknowledgment
Computations for Efficient Comparison of Two Methods (Comp_Meth Worksheet)
Summary
Chapter 39: Collaborative Laboratory Studies: Part 6—MathCad Worksheet Text
Abstract
Section 8: Analysis of Noise
Chapter 40: Is Noise Brought by the Stork? Analysis of Noise—Part 1—A Listing of the Sources of Spectroscopic Noise and Their Characteristics
Abstract
Chapter 41: Analysis of Noise—Part 2—The analysis of the effect of ‘constant’ detector noise on a transmission measurement
Abstract
Appendix: Proof That the Variance of a Sum Equals the Sum of the Variances
Chapter 42: Analysis of Noise—Part 3—The Analysis of the Effect of ‘constant’ Detector Noise on the Absorbance, the Relative Absorbance (ΔA/A) and the Optimum Absorbance Value
Abstract
Chapter 43: Analysis of Noise—Part 4—The Analysis of the Effect of ‘constant’ Gaussian Detector Noise When the Noise Is Not Negligible Compared to the Signal
Abstract
Chapter 44: Analysis of Noise—Part 5—The Analysis of the Effect of ‘constant’ Gaussian Detector Noise When the Reference Energy Approaches Zero
Abstract
Update
Continuation
Chapter 45: Analysis of Noise—Part 6—The Analysis of the Effect of ‘constant’ Gaussian Detector Noise: Comparing the Effect of Noise in the Sample Channel Versus Noise in the Reference Channel
Abstract
Chapter 46: Analysis of Noise—Part 7—The Analysis of ‘constant’ Detector Noise on the Kubelka-Munk Function
Abstract
Variation of the Reflectance Due to Noise
Noise of the KM Function
Expressing the Variations in Terms of Measured Energies
Passing Into the Statistical Domain
Optimizing the KM Function
Go to the Statistical Domain
Summary
Chapter 47: Analysis of Noise—Part 8—Effect of Noise on the Computed Transmittance, Analysis of Uniformly Distributed Noise for Transmittance and Absorbance Values
Abstract
Effect of Noise on Computed Transmittance
Computed Transmittance Noise
Chapter 48: Analysis of Noise—Part 9—Analysis of Poisson-Distributed Noise, Effects on Transmittance and Absorbance Values
Abstract
Chapter 49: Analysis of Noise—Part 10—Analysis of Poisson-Distributed Noise, Effects on Relative Absorbance
Abstract
Chapter 50: Analysis of Noise—Part 11—Analysis of Poisson-Distributed Noise, When the Noise Is Not Small Compared to the Reference Signal
Abstract
Discussion
Chapter 51: Analysis of Noise—Part 12—Analysis of Poisson-Distributed Noise: Computation of the Transmittance Noise
Abstract
Chapter 52: Analysis of Noise—Part 13—Analysis of Poisson-Distributed Noise: Computation of the Absorbance Noise
Abstract
Chapter 53: Analysis of Noise—Part 14—Analysis of Noise Proportional to the Signal, Small-Noise Case
Abstract
Chapter 54: Analysis of Noise—Part 15—Analysis of Noise Proportional to the Signal, Large-Noise Case
Abstract
Preliminary Steps
Evaluation of the Function
Noise
Section 9: Derivatives
Chapter 55: Derivatives in Spectroscopy: Part 1—The Behavior of the Theoretical Derivative
Abstract
The Behavior of Theoretical Derivatives
The Behavior of Computed Derivatives
Chapter 56: Derivatives in Spectroscopy: Part 2—The “True” Derivative
Abstract
Better Derivative Approximations
Chapter 57: Derivatives in Spectroscopy: Part 3—Computing the Derivative (the Savitzky-Golay Method)
Abstract
Methods of Computing the Derivative
Limitations of the Savitzky-Golay Method
Extensions to the Savitzky-Golay Method
Chapter 58: Derivatives in Spectroscopy: Part 4—Calibrating With Derivatives
Abstract
Acknowledgment
Chapter 59: Corrections and Discussion Regarding Derivatives
Abstract
Section 10: Goodness of Fit Statistics
Chapter 60: Comparison of Goodness of Fit Statistics for Linear Regression: Part 1—Introduction
Abstract
Chapter 61: Comparison of Goodness of Fit Statistics for Linear Regression: Part 2—The Correlation Coefficient
Abstract
Chapter 62: Comparison of Goodness of Fit Statistics for Linear Regression: Part 3—Computing Confidence Limits for the Correlation Coefficient
Abstract
Testing Correlation for Different Size Populations
Chapter 63: Comparison of Goodness of Fit Statistics for Linear Regression: Part 4—Confidence Limits for Slope and Intercept
Abstract
Section 11: More About Linearity In Calibration
Chapter 64: Linearity in Calibration, Act III Scene I: Importance of (Non)linearity
Abstract
Why Is Nonlinearity Important?
Chapter 65: Linearity in Calibration, Act III Scene II: A Discussion of the Durbin-Watson Statistic, a Step in the Right Direction
Abstract
Chapter 66: Linearity in Calibration, Act III Scene III: Other Tests for Nonlinearity
Abstract
F-test
Normality of Residuals
Chapter 67: Linearity in Calibration, Act III Scene IV: How Test for Nonlinearity
Abstract
Conclusion
Appendix A: Derivation and Discussion of the Formula in Eq. (67-11)
Chapter 68: Linearity in Calibration, Act III Scene V: Quantifying Nonlinearity
Abstract
Chapter 69: Linearity in Calibration, Act III, Scene VI: Quantifying Nonlinearity, Part II: A Calculus-Based Approach, and A News Flash
Abstract
News Flash!!
Section 12: Connecting Chemometrics To Statistics
Chapter 70: Connecting Chemometrics to Statistics: Part 1—The Chemometrics Side☆
Abstract
Chapter 71: Connecting Chemometrics to Statistics: Part 2—The Statistics Side
Abstract
Multivariate ANOVA
Section 13: Limitations In Analytical Accuracy
Chapter 72: Limitations in Analytical Accuracy: Part 1—Horwitz’s Trumpet
Abstract
Chapter 73: Limitations in Analytical Accuracy: Part 2—Theories to Describe the Limits in Analytical Accuracy
Abstract
Detection Limit for Concentrations Near Zero
Chapter 74: Limitations in Analytical Accuracy: Part 3—Comparing Test Results for Analytical Uncertainty
Abstract
Uncertainty in an Analytical Measurement
Comparison Test for a Single Set of Measurements Versus a True Analytical Result
Comparison Test for Two Sets of Measurements
Calculating the Number of Measurements Required to Establish a Mean Value (or Analytical Result) With a Prescribed Uncertainty (Accuracy)
The Q-Test for Outliers [1–3]
Summation of Variance From Several Data Sets
Chapter 75: The Statistics of Spectral Searches
Abstract
Common Spectral Matching Approaches
Mahalanobis Distance Measurements
Euclidean Distance
Common Spectral Matching (Correlation or Dot Product)
Chapter 76: The Chemometrics of Imaging Spectroscopy
Abstract
Image Projection of Spectroscopic Data
Chapter 77: Corrections to Analysis of Noise—Part 1: Alternate Analysis of Transmittance Noise in the ‘large noise’ Regime
Abstract
Alternate Analysis
Chapter 78: Corrections to Analysis of Noise—Part 2: Alternate Analysis of Absorbance noise in the ‘large noise’ Regime
Abstract
Absorbance Noise in the “High-Noise” Regime
Chapter 79: What Can NIR Predict?
Abstract
Part A—Results From a Model Created Using Real Reference Values
Part B—Results from a Model Created Using Random Reference Values
Part C—So What Does It All Mean?
Section 14: Derivations of Principal Components
Chapter 80: The Long, Complicated, Tedious, and Difficult Route to Principal Components (or, When You’re Through Reading This Set You’ll Know Why It's Always Done With Matrices)—Part 1: Introduction and Review
Abstract
Abstract
Some Preliminary Discussion
Some Preliminary Results
Application of the ANOVA Principle
Appendix A: Matrices and Matrix Notation
Chapter 81: The Long, Complicated, Tedious, and Difficult Route to Principal Components (or, When You’re Through Reading This Set You’ll Know Why It's Always Done With Matrices)—Part 2: Our First Attempt
Abstract
Chapter 82: The Long, Complicated, Tedious, and Difficult Route to Principal Components (or, When You’re Through Reading This Set You’ll Know Why It's Always Done With Matrices)—Part 3: Multivariate Curve Fitting
Abstract
Appendix B
Chapter 83: The Long, Complicated, Tedious, and Difficult Route to Principal Components (or, When you’re Through Reading This Set You’ll Know Why It's Always Done With Matrices)—Part 4: The Lagrange Multiplier
Abstract
Appendix C
Chapter 84: The Long, Complicated, Tedious, and Difficult Route to Principal Components (or, When you’re Through Reading This Set You’ll Know Why It's Always Done With Matrices)—Part 5: Solving the Equations With Determinants
Abstract
Chapter 85: The Long, Complicated, Tedious, and Difficult Route to Principal Components (or, When You’re Through Reading This Set You’ll Know Why It's Always Done With Matrices)—Part 6: Solving the Equations Without Determinants
Abstract
Summary
Chapter 86: The Long, Complicated, Tedious, and Difficult Route to Principal Components (or, When You’re Through Reading This Set You’ll Know Why It's Always Done With Matrices)—Coda: Applying Constrained Univariate Calculations
Abstract
Section 15: Clinical Data Reporting
Chapter 87: Statistics and Chemometrics for Clinical Data Reporting, Part 1—Fundamentals
Abstract
Acknowledgments
Introduction
Definitions
Measurement Error
Accuracy
Trueness and Bias
Precision
Sample Data Calculations
Computation of the Regression Line
Chapter 88: Statistics and Chemometrics for Clinical Data Reporting, Part 2 (Using Excel for Computations)
Abstract
Acknowledgments
Introduction
Basic Definitions
Measurement Error
Accuracy
Trueness and Bias
Standard Deviation (as a Measure of Repeatability)
Precision
Computation of the Regression Line
Linear Regression Corrected Results
Chapter 89: Statistics and Chemometrics for Clinical Data Reporting, Part 3 (Using Excel for Data Plotting)
Abstract
Introduction
Comparing a Reference Method to a Second Test Method
Correcting One Analytical Method to Report Values of a Second Method
Comparisons of Two Analytical Methods
Section 16: Classical Least Squares (CLS)
Chapter 90: Classical Least Squares, Part 1: MathematicalTheory
Abstract
The Mathematics Behind the CLS Method
Chapter 91: Classical Least Squares, Part 2: Mathematical Theory Continued
Abstract
Chapter 92: Classical Least Squares, Part 3: Spectroscopic Theory
Abstract
Equivalence of Spectra and Numbers
Chapter 93: Classical Least Squares, Part 4: Spectroscopic Theory Continued
Abstract
Chapter 94: Classical Least Squares, Part 5: ExperimentalResults
Abstract
Experimental
The Data
Chapter 95: Classical Least Squares, Part 6: Spectral Results
Abstract
Chapter 96: Classical Least Squares, Part 7: Spectral Reconstruction of Mixtures
Abstract
Adding Some Calculations
Chapter 97: Classical Least Squares, Part 8: Comparison of CLS Values With Known Values
Abstract
Troubleshooting
The Search for Composition
Chapter 98: Classical Least Squares, Part 9: Spectral Results from a Second Laboratory
Abstract
Spectral Results
Chapter 99: Classical Least Squares, Part 10: Numerical Results From the Second Laboratory
Abstract
Chapter 100: Classical Least Squares, Part 11: Comparison of Results From the Two Laboratories (Continued)
Abstract
The Light Dawns
Section 17: Transfer of Calibrations
Chapter 101: Transfer of Calibrations, Part 1: An Overview
Abstract
Introduction
Experimental
Results
Discussion
Conclusions
Definition
Summary
Chapter 102: Calibration Transfer, Part 2: The Instrumentation Aspects
Abstract
Introduction
Modeling Approaches
Instrument Types
Standardization Methods
Instrument Comparison and Evaluation Methods
Instrument Optical Quality Performance Tests
Chapter 103: Calibration Transfer, Part 3: The Mathematical Aspects
Abstract
Introduction
The Mathematical Approaches to Calibration Transfer
What to Compare When Transferring Calibrations?
Summary
Chapter 104: Calibration Transfer, Part 4: Measuring the Agreement Between Instruments Following Calibration Transfer
Abstract
Introduction
How to Tell If Two Instrument Predictions, or Method Results, Are Statistically Alike
Standard Uncertainty and Relative Standard Uncertainty
Relative Standard Uncertainty
Bland-Altman “Limits of Agreement”
Conclusion
Chapter 105: Calibration Transfer, Part 5: The Mathematics of Wavelength Standards Used for Spectroscopy
Abstract
Introduction
Different Approaches to Alignment of the Wavelength Axis
The NIST Uncertainty Number for SRMs
Commercial Instrument Wavelength Data
The Relative Standard Uncertainty Across Commercial Instruments
The Confidence Levels and Uncertainty Across Commercial Instrument Manufacturers A Through D (Table 105-3)
Using a NIST-Like Uncertainty Measurement
Chapter 106: Calibration Transfer, Part 6: The Mathematics of Photometric Standards Used for Spectroscopy
Abstract
Introduction
Different Approaches to Alignment of the Photometric Axis
The NIST Uncertainty Number for SRMs
Commercial Instrument Photometric Data
Estimating the Relative Uncertainty Across the Tested Commercial Instruments
Section 18: The Importance of Units of Measure
Chapter 107: Units of Measure in Spectroscopy, Part 1: … and Then the Light Dawned
Abstract
Chapter 108: Units of Measure in Spectroscopy, Part 2:It's the VOLUME, Folks!
Abstract
Progress
Chapter 109: Units of Measure in Spectroscopy, Part 3: What Does It All Mean
Abstract
A Short Review
The Mathematics
Chapter 110: Units of Measure in Spectroscopy, Part 4: Summary of Our Findings
Abstract
Summary of Our Findings
CLS Versus Beer's Law
Meaning of “Concentration”
Calibration Transfer
Partial Molal Volumes
Chapter 111: Units of Measure in Spectroscopy, Part 5: The “Mythbusters” and Spectral Reconstruction
Abstract
Calculation of Pure-Component Spectra
Section 19: The Best Calibration Model
Chapter 112: Choosing the Best Regression Model
Abstract
Introduction
Optimizing the Experimental Design for Calibration
Validating the Regression Model
Summary
Chapter 113: Optimizing the Regression Model: The Challenge of Intercept/Bias and Slope “Correction”
Abstract
Introduction
The Initial Spectra and Calibration Equation
Changing the Wavelength Registration
Changing the Photometric Registration
Changing the Linewidth/Lineshape
Discussion and Review of Results
Summary of Results
Section 20: Statistics
Chapter 114: Statistics, Part 1: First FoundatioN: Probability Theory
Abstract
Introduction
The Foundations
Chapter 115: Statistics, Part 2:Second FoundatioN: Analysis of Variance
Abstract
Second Foundation: The Principle of Analysis of Variance
Analysis of Variance of Synthetic Data
The Effect of Adding a Perturbation
Chapter 116: Statistics, Part 3: Third FoundatioN: Least Squares
Abstract
Third Foundation: The Principle of Least Squares
Chapter 117: How to Select the Appropriate Degrees of Freedom for Multivariate Calibration
Abstract
Introduction
The Subject of Degrees of Freedom
General Discussion
Summary
Chapter 118: Bias and Slope Correction
Abstract
Introduction
Analysis of the Issues
Instrument Differences Versus SEP
Instrument Differences Versus Bias
Instrument Differences Versus Slope
Summary
Section 21: Outliers
Chapter 119: Outliers—Part 1: What Are Outliers?
Abstract
Introduction
What Are Outliers?
Chapter 120: Outliers—Part 2:Pitfalls in Detecting Outliers
Abstract
Detecting Outliers
Potential Pitfalls
Chapter 121: Outliers—Part 3: Dealing With Outliers
Abstract
Dealing With Outliers
Summary
Section 22: Spectral Transfer: Making Instruments Agree
Chapter 122: Calibration Transfer Chemometrics, Part 1: Review of the Subject
Abstract
Introduction
Comparison of Transfer Methods
Instrument Alignment and Correction
The Master Instrument Concept
Filter Instrument Calibration Transfer
Direct Standardization and Piecewise Direct Standardization
Orthogonal Signal Correction
Procrustes Analysis
Finite Impulse Response
Maximum Likelihood Principal Component Analysis
Using Wavelength Standards for FT-NIR Alignment
Summary
Chapter 123: Calibration Transfer Chemometrics, Part 2: Review of the Subject
Abstract
Introduction
Summary
Section 23: Applying Standard Reference Materials
Chapter 124: Using Reference Materials, Part 1: Standards for Aligning the x-Axis
Abstract
Introduction
Ultraviolet Wavelength Measurement Standards
Visible (Vis) Wavelength Measurement Standards
Near Infrared (NIR) Wavelength Measurement Standards
Infrared Wavenumber Measurement Standards
Raman Wavenumber Measurement Standards
Summary
Chapter 125: Using ReferenceMaterials, Part 2:Aligning the y-Axis
Abstract
Introduction to Photometric Accuracy
Photometric Correction for Absorbance-Based Spectrophotometers
Ultraviolet Photometric Standards
Visible (Vis) Photometric Standards
Near Infrared Reflectance Photometric Standards
Infrared Reflectance Photometric Standards
Raman Intensity Correction Standards
Section 24: More About CLS
Chapter 126: More about cls, part 1: expanding the concept
Abstract
Expanding Beyond the Small Dataset
Faux Linearity
Chapter 127: More About CLS, Part 2: Spectral Results and CLS (Not Requiring Constituent Values)
Abstract
Spectral Results Not Needing Constituent Values
CLS Results
Expanding Beyond CLS: Introducing PCA
Chapter 128: More About CLS, Part 3: Expanding the Analysis to Include Concentration Information (PCR and PLS)
Abstract
Expanding the Analysis to Include Concentration Information (PCR and PLS)
Effect of PCR Analysis
Effect on PLS Analysis
Results for Individual Analytes
Conclusions Reached
Index

Howard Mark

Howard Mark is President of Mark Electronics, Suffern, New York. He was previously affiliated as a Senior Scientist at Technicon Instrument Corp. in Tarrytown, New York. He holds a B.S. degree from City College of New York, an M.A. from City University of New York, and a PhD from New York University. His professional interests include instrument development, especially for spectroscopy; statistical and chemometric data analysis; and Custom software development, especially for implementation of data analysis algorithms. He received the 2003 Eastern Analytical Symposium Award for Achievement in Near Infrared Spectroscopy. He holds 6 U.S patents and has published 2 books and numerous book chapters. He has acted as Associate editor for the Handbook of Vibrational Spectroscopy, Wiley (2001). He has served as Past president of Council for Near-Infrared Spectroscopy (CNIRS), Treasurer of the New York section of the Society for Applied Spectroscopy, and as Past Chair of the New York section of the Society for Applied Spectroscopy. In addition he acts as Contributing editor and member of the Editorial Advisory Board of Spectroscopy. He has published over 150 peer-reviewed papers dealing with design and development of scientific instrumentation, new concepts in computerized instrumentation and data analysis.

Affiliations and expertise

Mark Electronics, Suffern, NY, USA

Jerry Workman Jr.

Jerome (Jerry) J. Workman, Jr. is Executive Vice President of Research & Engineering for Unity Scientific and Process Sensors Corporation; Certified Core Adjunct Professor at National University, CA; and Principal at Biotechnology Business Associates. He was formerly Vice President of Technology Research for Masimo Corporation; Director of Research, Technology & Applications Development for Molecular Spectroscopy & Microanalysis for ThermoFisher Scientific; Chief Technical Officer and Vice President of Research & Engineering at Argose Inc.; Senior Research Fellow at Kimberly-Clark Analytical Science & Technology; and Principal Scientist at Perkin-Elmer. Dr. Workman has played a major role in defining and developing over 20 scientific instrument advancements with novel software improvements for successful commercial use for start-ups to major corporations. He has more than 55 U.S. and international patent applications (since 1998); 20 U.S. and international patents issued, and multiple trade secrets. He has a total of 475 technical publications; and 18 reference books on a broad range of spectroscopy, chemometrics, and data processing techniques. He has received awards from the Eastern Analytical Symposium, ASTM International, Coblentz Society; as well as multiple fellowships, technical, and government appointments. He has taught annual courses in NIR spectroscopy, chemometrics, and statistics for the Association of Official Analytical Chemists, the American Chemical Society, the Instrument Society of America, and the Federation of Analytical Chemists and Spectroscopy Societies, and at several universities and corporations. He holds a BA degree cum laude in natural sciences, and an MA in biological sciences from Saint Mary's University of Minnesota, and a PhD degree with high commendation in biological chemistry from Columbia Pacific University. He is a graduate of the Columbia Senior Executive Program and also holds Columbia Business School Certificates in Executive Development (CIED) and in Business Excellence (CIBE). He also holds a Certificate in Strategy and Innovation from the M.I.T. Sloan School. He is listed in Who's Who in the World, Who's Who in America, and Who's Who in Science and Engineering.

Affiliations and expertise

Unity Scientific and Process Sensors Corporation, Milford, MA; National University, San Diego, CA; and Biotechnology Business Associates, Milford, MA, USA

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